There’s no mincing words here. Missing values can cause problems for every statistician. That’s true for a lot of reasons, but it can start with simple issues of choices made when coding missing values in a data set. Here are a few examples.
Example 1: The Null License Plate
Researcher Joseph Tartaro thought it would be funny to get the following California vanity license plate: (more…)
Some approaches to missing data work well in some situations, but perform very poorly in others. So it’s really important to get a good idea of the type and pattern of missingness in your data. You may even take different missing data approaches to different variables.
Matt Blackwell of the Harvard Social Science Statistics blog has come up with a nice way to visualize the missingness patterns in a data set. (I’m a big fan of graphing data to understand it). He calls it a Missingness Map.
The only drawback seems to be that it will be cumbersome for large data sets.